Wireless sensor networks provide efficient and far reaching means for monitoring various environmental conditions, including sound, temperature, motion, etc. While wireless sensor networks were originally developed for military applications, such networks also have a wide range of commercial applications. Thus, there is significant interest in improving the efficiency and/or accuracy of sensor networks.
One exemplary sensor network used for tracking targets is described in “Sensory Scheduling Using a 0-1 Mixed Integer Programming Framework” by A. Chhetri, D. Morrell, and A. Papandreou-Suppappola. This sensor network includes a plurality of sensor nodes that wirelessly communicate with a central hub. The individual sensor nodes measure acoustic energy and make hard decisions regarding the presence (or lack thereof) of a target. Each sensor node reports its decision to the central control hub with a single bit. The hub aggregates the decisions received from each sensor node to determine the target's location. The hub may also implement a scheduling algorithm that schedules when each sensor node should be active. In so doing, the hub increases the battery life of the sensors.
By reporting the decision to the central hub, the sensor nodes significantly simplify the signals communicated to the central hub, which in turn reduces the power requirements for each sensor node. While reporting the hard decision to the hub helps conserve the sensor nodes' battery power, the reported decisions often limit the ability of the central hub to fully evaluate a sensed condition. For example, while the reported hard decisions may enable the central hub to detect a target, the hard decisions may not enable the central hub to track a target or determine a characteristic of the target, such as the target size.